Skip to main content
SpringerLink
Log in

Executable Knowledge

  • Reference work entry
Encyclopedia of Database Systems

  • 100 Accesses

Synonyms

Computer-interpretable formalism; Knowledge-based systems

Definition

Executable knowledge is represented in a symbolic formalism that can be understood by human beings and interpreted and executed by a computer program. It allows a computer program to match case data to the knowledge, reason with the knowledge, select recommended actions that are specific to the case data, and deliver them to users. Executed knowledge can be delivered in the form of advice, alerts, and reminders, and can be used in decision-support or process management.

Historical Background

Representing knowledge in a computer-interpretable format and reasoning with it so as to support humans in decision making started to be developed by the artificial intelligence community in the 1970s. According to Newell [6], knowledge is separate from its representation. At the knowledge level, an agent has as parts bodies of knowledge, actions, and goals. An agent processes its knowledge to determine the actions to...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 2,500.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  1. Davis R., Shrobe H., and Szolovits P. What is a knowledge representation? AI Magazine, 14(1):17–33, 1993.

    Google Scholar 

  2. de Clercq P.A., Blom J.A., Korsten H.H.M., and Hasman A. Approaches for creating computer-interpretable guidelines that facilitate decision support. Artif. Intell. Med., 31:1–27, 2004.

    Article  Google Scholar 

  3. Gruber T.R. Toward principles for the design of ontologies used for knowledge sharing. Int. J. Human Comput. Stud., 43:907–928, 1995.

    Article  Google Scholar 

  4. Hripcsak G., Ludemann P., Pryor T.A., Wigertz O.B., and Clayton P.D. Rationale for the arden syntax. Comput. Biomed. Res., 27(4):291–324, 1994.

    Article  Google Scholar 

  5. Musen M.A., Tu S.W., Eriksson H., Gennari J.H., and Puerta A.R. PROTEGE-II: an environment for reusable problem-solving methods and domain ontologies. In Proc. 13th Int. Joint Conf. on AI, 1993.

    Google Scholar 

  6. Newell A. The knowledge level. AI Magazine, 2(2):1–20, 33, 1980.

    Google Scholar 

  7. Pearl J. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco, CA, 1988.

    Google Scholar 

  8. Peleg M. Guideline and workflow models. In Clinical Decision Support – The Road Ahead, R.A. Greenes (ed.). Elsevier/Academic Press, Orlando, FL, 2006.

    Google Scholar 

  9. Peleg M., Keren S., and Denekamp Y. Mapping computerized clinical guidelines to electronic medical records: knowledge-data ontological mapper (KDOM). J. Biomed. Inform., 41(1):180–201, 2008.

    Article  Google Scholar 

  10. Peleg M., Tu S.W., Bury J., Ciccarese P., Fox J., Greenes R.A., et al. Comparing computer-interpretable guideline models: a case-study approach. J. Am. Med. Inform. Assoc., 10(1):52–68, 2003.

    Article  Google Scholar 

  11. Shortliffe E.H. Computer-Based Medical Consultations: Mycin. Elsevier/North Holland, New York, 1976.

    Google Scholar 

  12. Sordo M., Ogunyemi O., Boxwala A.A., Greenes R.A., and Tu S. GELLO: a common expression language. Available online at: http://cslxinfmtcs.csmc.edu/hl7/arden/2004–09-ATL/v3ballot_gello_aug2004.zip, 2004.

  13. ten Teije A., Marcos M., Balser M., van Croonenborg J., Duelli C., van Harmelen F., et al. Improving medical protocols by formal methods. Artif. Intell. Med., 36(3):193–209, 2006.

    Article  Google Scholar 

  14. Tu S.W., Campbell J.R., Glasgow J., Nyman M.A., McClure R., McClay J.P.C., Hrabak K.M., Berg D., Weida T., Mansfield J.G., Musen M.A., and Abarbanel R.M. The SAGE guideline model: achievements and overview. J. Am. Med. Inform. Assoc., 14(5):589–598, 2007.

    Article  Google Scholar 

  15. van der Aalst W.M.P. The application of petri nets to workflow management. J. Circuits Syst. Comput., 8(1):21–66, 1998.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Haifa, Haifa, Israel

    Mor Peleg

Authors
  1. Mor Peleg
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Peleg, M. (2009). Executable Knowledge. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1356

Download citation

Publish with us

Policies and ethics

Search

Navigation